Large data sets may exist in various sizes and organizational structures. With big data comprising data sets as large as ever, the volume of data collected incident to the increased popularity of online and electronic transactions continues to grow. For example, billions of records (also referred to as rows) and hundreds of thousands of columns worth of data may populate a single table. An example of the use of large data is in assembling test data sets to perform analysis of transaction data, which is frequently a key priority for transaction account issuers. In that regard, transactions processed by the transaction account issuer are massive in volume and comprise tremendously large data sets.
Large data sets may have challenges. For example, a user may wish to retrieve a test data set for analysis of transaction data. The test data set may comprise a subset of the larger data set, and the user may wish to limit the test data set to a subset of fields and/or attributes otherwise available in a large data set. The process of sorting and filtering the large data set to conform to the desired limitations may be time consuming and may also use a large amount of computing resources, particularly if the data is desired to be updated at some interval. Moreover, different test environments may require different database structures and requirements and may comprise different desired limitations. These limitations often hamper the availability of test data sets, result in the use of stale test data, and confuse the analysis of the transaction data.